Narrow Results

This research focuses on developing a new decision-making model to evaluate school reopening strategies during the COVID-19 pandemic. The model integrates deep learning and factor analysis to address the urgent need to restart educational services without worsening the health crisis. It starts by gathering time series data from various districts to apply deep learning for predicting virus dynamics, emphasizing feature extraction and hyperparameter optimization. The subsequent phase involves factor analysis to discover key factors influencing virus spread, using outputs from the deep learning step. Based on these factors, clustering methods then sort districts into controllable or vulnerable groups. The final stage combines these analyzes into a deterministic decision model aiding policymakers in crafting school reopening guidelines. The model identifies three primary controllable factors: infection growth rate, reduction in active cases, and lowered mortality rates. Clustering then reveals that three groups are controllable, enabling specific interventions. This model is noteworthy for considering causal links between pandemic metrics and its adaptability to diverse datasets across districts/subdistricts, offering a scalable solution for decision-makers. The results highlight the importance of local infection trends and tailored data in shaping policies, showing that strong predictive analytics and insight into significant factors are crucial for developing effective, safe school reopening plans.

Purpose: This research aims to examine and study the direct impact of change in COVID-19 cases and IIP index on the performance of NIFTY and selected mutual funds NAV performance for one year from the date WHO declared COVID-19 pandemic on 11 March 2020.

Approach/Methodology/Design: The study applies using daily NAV series of four mutual funds across sectors. ANOVA, Correlation, Regression, Descriptive Statistics.

Findings: There is a strong relation among NIFTY 50 and all the four mutual funds’ NAV performance, however, the relation between daily reported COVID-19 cases and NAV performance couldn’t be established.

Practical Implications: Economic turmoil has affected the disposable income of investors, the capital market is very volatile. This study will help the researchers and analysts to understand the relationship between COVID-19, NIFTY 50 index and select mutual funds NAV movements. And they can make a better decision under a similar situation.

The Susceptible, Infected and Recover (SIR) model is a very simple model to estimate the dynamics of an epidemic. In the current pandemic due to Covid-19, the SIR model has been used to estimate the dynamics of infection for various infected countries. Numerical solutions are used to obtain the value of parameters for the SIR model. The maximum and minimum basic reproduction number (14.5 and 2.3) are predicted to be in Turkey and China, respectively.

The Susceptible, Infected and Recover (SIR) model is a very simple model to estimate the dynamics of an epidemic. In the current pandemic due to Covid-19, the SIR model has been used to estimate the dynamics of infection for Bangladesh, India, Pakistan and compared with that of China. Numerical solutions are used to obtain the value of parameters for the SIR model. It is predicted that the active case in Pakistan due to the SARS-CoV-2 will be comparable with that in China whereas it will be low for Bangladesh and India. The basic reproduction number, with fluctuations, for South Asian countries are predicted to be less than that of China. The susceptible population is also estimated to be under a million for Bangladesh and India but it becomes very large for Pakistan.

Between the years 2020 and 2022, the world was hit by the pandemic of COVID-19 giving rise to an extremely grave situation. Many people suffered and died from this disease. Also, the global economy was badly hurt due to the consequences of various intervention strategies (like social distancing, lockdown) which were applied by different countries to control this pandemic. There are multiple speculations that humanity will again face such pandemics in the future. Thus, it is very important to learn and gain knowledge about the spread of such infectious diseases and the various factors which are responsible for it. In this study, we have extended our previous work [S. Chowdhury, S. Roychowdhury and I. Chaudhuri, Eur. Phys. J. Spec. Top. 231, 3619 (2022), doi:10.1140/epjs/s11734-022-00619-1] on the probabilistic cellular automata (CA) model to reproduce the spread of COVID-19 in several countries by modifying its earlier used neighborhood criteria. This modification gives us the liberty to adopt the effect of different restrictions like lockdown and social distancing in our model. We have done some theoretical analysis for initial infection and simulations to gain insights into our model. We have also studied the data from eight countries for COVID-19 in a window of 876 days and compared it with our model. We have developed a proper framework to fit our model on the data for confirmed cases of COVID-19 and have also re-checked the goodness of the fit with the data of the deceased cases for this pandemic. Our model is compared with other well-known CA models and the ODE-based SEIR model. This model fits well with different peaks of COVID-19 data for all the eight countries and can be possibly generalized for a global prediction. Our study shows that the rate of disease spread depends both on infectivity of a disease and social restrictions. Also, it shows an overall decrement in mortality rate with time due to COVID-19 as more and more people get infected as well as vaccinated. Our minimal model with modified neighborhood condition can easily quantify the degree of social restrictions. It is statistically concluded that the overall degree of social restrictions is above the mean when we considered all eight countries. Finally to conclude, this study has given us various important insights about this pandemic which can help in preparing for combating epidemics in future situations.

We provide novel evidence on how COVID-19 affected overall life satisfaction using a monthly longitudinal survey of middle-aged and older Singaporeans. We study how the subjective well-being of individuals evolves over the course of 18 months including the outbreak of the pandemic, the implementation of the lockdown and the spike of cases due to the delta variant in a country where COVID-19 is controlled in a sustained manner. Using an event-study design framework, we find large declines in overall life satisfaction in the lead-up to and following the lockdown. Fifteen months after the outbreak of the pandemic, and 13 months out from the end of lockdown, individuals have nearly, though not fully, adapted to living with the virus. We find greater negative well-being impacts of COVID-19 among individuals who report a drop in household income during the COVID-19 outbreak compared to those who do not report any income loss. However, we find little evidence of heterogeneity in the dynamics of the recovery in well-being by individuals’ underlying health status, marital status and education. On personality types, people who are high in neuroticism experience larger dips in well-being during the lockdown, and adapt to living with COVID-19 at a slower rate.

Since the onset of the COVID-19 outbreak in Wuhan, China, numerous forecasting models have been proposed to project the trajectory of coronavirus infection cases. Most of these forecasts are based on epidemiology models that utilize deterministic differential equations and have resulted in widely varying predictions. We propose a new discrete-time Markov chain model that directly incorporates stochastic behavior and for which parameter estimation is straightforward from available data. Using such data from China’s Hubei province (for which Wuhan is the provincial capital city and which accounted for approximately 82% of the total reported COVID-19 cases in the entire country), the model is shown to be flexible, robust, and accurate. As a result, it has been adopted by the first Shanghai assistance medical team in Wuhan’s Jinyintan Hospital, which was the first designated hospital to take COVID-19 patients in the world. The forecast has been used for preparing medical staff, intensive care unit (ICU) beds, ventilators, and other critical care medical resources and for supporting real-time medical management decisions.

In this paper, we have proposed and analyzed a simple model of Influenza spread with an asymptotic transmission rate. Existence and uniqueness of solutions are established and shown to be uniformly bounded for all non-negative initial values. We have also found a sufficient condition which ensures the persistence of the model system. This implies that both susceptible and infected will always coexist at any location of the inhabited domain. This coexistence is independent of values of the diffusivity constants for two subpopulations. The global stability of the endemic equilibrium is established by constructing a Lyapunov function. By linearizing the system at the positive constant steady-state solution and analyzing the associated characteristic equation, conditions for Hopf and Turing bifurcations are obtained. We have also studied the criteria for diffusion-driven instability caused by local random movements of both susceptible and infective subpopulations. Turing patterns selected by the reaction–diffusion system under zero flux boundary conditions have been explored.

Numerical simulations show that contact rate, β which is related to the reproduction number , plays an important role in spatial pattern formation. It was found that diffusion has appreciable influence on spatial spread of epidemics. The wave of chaos appears to be a dominant mode of disease dispersal. This suggests a bidirectional spread for influenza epidemics. The epidemic propagates in the form of nonchaotic and chaotic waves as observed in H1N1 incidence data of positive tests in 2009 in the United States. We have conducted numerical simulations to confirm the analytic work and observed interesting behaviors. This suggests that influenza has a complex dynamics of spatial spread which evolves with time.

Lockdown and vaccination policies have been the major concern in the last year in order to contain the SARS-CoV-2 infection during the COVID-19 pandemic. In this paper, we present a model able to evaluate alternative lockdown policies and vaccination strategies. Our approach integrates and refines the multiscale model proposed by Bellomo et al., 2020, analyzing alternative network structures and bridging two perspectives to study complexity of living systems. Inside different matrices of contacts we explore the impact of closures of distinct nodes upon the overall contagion dynamics. Social distancing is shown to be more effective when targeting the reduction of contacts among and inside the most vulnerable nodes, namely hospitals/nursing homes. Moreover, our results suggest that school closures alone would not significantly affect the infection dynamics and the number of deaths in the population. Finally, we investigate a scenario with immunization in order to understand the effectiveness of targeted vaccination policies towards the most vulnerable individuals. Our model agrees with the current proposed vaccination strategy prioritizing the most vulnerable segment of the population to reduce severe cases and deaths.

At present, H5N1 avian influenza (AI) is a zoonotic disease where the transmission to humans occurs from infected domestic birds. Since 2003, more than 500 people have been infected and nearly 60% of them have died. If the H5N1 virus becomes efficiently human-to-human transmittable, a pandemic will occur with potentially high mortality. A mathematical model of AI, which involves human influenza, is introduced to better understand the complex epidemiology of AI and the emergence of a pandemic strain. Demographic and epidemiological data on birds and humans are used for the parameterization of the model. The differential equation system faithfully projects the cumulative number of H5N1 human cases and captures the dynamics of the yearly cases. The model is used to rank the efficacy of the current control measures used to prevent the emergence of a pandemic strain. We find that culling without re-population and vaccination are the two most efficient control measures each giving 22% decrease in the number of H5N1 infected humans for each 1% change in the affected parameters (μ_b, ν_b for culling and β_b, ν_b for vaccination). Control measures applied to humans, such as wearing protective gear, are not very efficient, giving less than 1% decrease in the number of H5N1 infected humans for each 1% decrease in β_Y, the bird-to-human transmission coefficient of H5N1. Furthermore, we find that should a pandemic strain emerge, it will invade, possibly displacing the human influenza virus in circulation at that time. Moreover, higher prevalence levels of human influenza will obstruct the invasion capabilities of the pandemic H5N1 strain. This effect is not very pronounced, as we find that 1% increase in human influenza prevalence will decrease the invasion capabilities of the pandemic strain with 0.006%.

Ebola outbreaks in Africa have occurred mostly in the Central and West Africa regions that are politically identified as the Economic Community of Central African States (ECCAS) and Economic Community of Western African States (ECOWAS), respectively. In the ECOWAS region, people and goods are allowed to travel freely across national borders of all the 15 member countries, but in the ECCAS region such regional travel across the national borders of its 10 member countries is limited. In this paper, we use parameterized mathematical models of Ebola to investigate the effects of free international travel, and the timing of border closings, on the high number of Ebola infection cases and deaths of the recent 2014–2016 Ebola outbreaks in Guinea, Liberia and Sierra Leone (ECOWAS); as compared to previous and current outbreaks in Democratic Republic of Congo (ECCAS, 1976–2018). Simulations of our single-patch Ebola model without movement of humans across international borders are shown to capture the recorded numbers of Ebola infections and deaths in the ECCAS region, and simulations of our 3-patch model with interpatch movements capture that of the ECOWAS region. We obtain that international travel restrictions and timing of border closings can play important roles in mitigating against the spread of future fatal infectious disease outbreaks.

In this paper, we investigated the impact of the COVID-19 pandemic on the cryptocurrency market. The direction of information transfer between the 67 digital cryptocurrency markets was evaluated, in particular Bitcoin, Ethereum, Litecoin, and Ripple, and we determined which of them were the most influential in the markets. The comparison of the first half of 2019 (outside the pandemic of COVID-19) against the first semester of 2020 (during the COVID-19 pandemic) was used to analyze the pandemic influence. We found two distinct behaviors: (i) in 2019, Bitcoin, as the primary capitalization bond, presented a more substantial transfer of information than the other cryptocurrencies toward Bitcoin $\to$ Ripple (0.0541), followed by Litecoin $\to$ Ripple (0.0522); (ii) in 2020, the most substantial transfers of information occurred from Ethereum to other cryptocurrencies (Litecoin, Bitcoin, and Ripple, in that order). In this period, the weakest transfers happened from Litecoin $\to$ Ripple and in the opposite direction, with equal value (0.0104). Our results indicate that there was a change in the direction of the information flow between the investigated cryptocurrencies, where ETH became the dominant cryptocurrency during the period of turbulence caused by the COVID-19 pandemic.

MALAYSIA — Veolia expands presence in East Malaysia.

SINGAPORE — Syneron Dental Lasers signs distribution agreement with Healthcare Solutions & Services Pte Ltd.

SINGAPORE — Fujitsu advances healthcare innovation in collaboration with National University of Singapore.

SINGAPORE — Clearbridge BioMedics makes a big impact at the 2012 Asian Innovation Awards.

SINGAPORE — TauRx Pharmaceuticals receives $111.8m commitment from Genting to prepare for Market Leadership in Alzheimer's.

THAILAND — Key Phase II HIV/HCV trial has commenced in Bangkok.

AUSTRALIA — Hatchtech mechanism of action data and safety study published.

AUSTRALIA — Power to you: carbon nanotube muscles are going strong.

EUROPE — GE Healthcare Life Sciences opens new £3 million laboratories for cell science.

EUROPE — AstraZeneca announces Phase III results from naloxegol pivotal trials.

EUROPE — ACADIA's pimavanserin sees Phase III success.

EUROPE — Big Pharma is doing more for access to medicine in developing countries.

EUROPE — CAVATAK™ bladder cancer – positive preliminary data.

EUROPE — Avita Medical initiates European trial in the management of chronic lower limb ulcers.

NORTH AMERICA — FEI unveils broad correlative microscopy solution set for cell biologists.

NORTH AMERICA — A single dose of Medicago's H5N1 VLP vaccine protects against additional pandemic flu strains in a preclinical study.

NORTH AMERICA — Biologics and stem cell research boost the cell culture market.

BioScience Managers banks on growth of anti-infectives market.

Rodin Therapeutics applies insights of epigenetics to neurological disorders.

LBT Innovations finalizes joint venture to drive global production of world-class automated diagnostics technology.

Phylogica expands collaborations with Janssen for peptide-drug conjugates.

Immune Design and Medicago announce license agreement and collaboration to develop novel adjuvanted pandemic influenza vaccines.

Bayat Foundation inaugurates new pediatric critical care facility at Indira Gandhi Hospital in Kabul.

AUSTRALIA – Origins of plague: Scientists reveal the cause of one of the most devastating pandemics in human history.

AUSTRALIA – Admedus releases interim phase I results for Herpes study.

CAMBODIA – Study tags cause of malaria drug resistance in Cambodia.

JAPAN – Discovery of mechanism by which sex hormone regulates aggressive behavior.

SINGAPORE – Singapore's first influenza vaccines demonstrates favorable immunogenicity and tolerability in clinical testing.

SINGAPORE – Scientists from Genome Institute of Singapore and Stanford University show RNA architecture expanding understanding of human genetics.

SINGAPORE – “Bio-Timer” that synchronizes growth.

SINGAPORE – Researchers make new discovery of protein as a promising target for treatment of anaplastic thyroid carcinoma.

AFRICA – African project aims to stop rats in their tracks.

AFRICA – African monsoon project to benefit crops and healthcare.

CANADA – Cancer researchers discover pre-leukemic stem cell at root of AML relapse.

EUROPE – Understanding heart failure at the cellular level.

INDIA – Africa and India cultivate agricultural research ties.

UNITED STATES – Three major genes set feather hue in pigeons.

UNITED STATES – Mouse study shows gene therapy may be possible cure for Hurler syndrome.

UNITED STATES – The ultimate decoy: Scientists find protein that helps bacteria misdirect immune system.

The following topics are under this section:

• 10 minutes to screen 4,000 drugs
• Remdesivir: Humanity’s hope against COVID-19?
• Epidemiology of COVID-19 and Perspectives from Chinese Medicine
• Tiding of the Outbreak

The following topics are under this section:

• China Market Access for MedTech via Hong Kong and the Greater Bay Area Initiative
• Don’t Panic, Be Cautious, and Together We Can Stop the Coronavirus Epidemic! — Supplementary Charts
• CytoMed Therapeutics – Pioneer in Advancing Cellular Immunotherapy against Cancer

The following topics are under this section:

• COVID-19 and its Impact on a Global and Societal Scale
• Possible, Immediate Solution for COVID-19: A position paper
• Surviving COVID-19
• Fundamentals to Help Hospitals in ASEAN stay Resilient

The following topics are under this section:

• Scaling Up the Digital Health Ecosystem
• Cohort Analysis of Remdesivir as Antiviral Treatment for COVID-19
• Pharmaceutical Giants Join Forces in Fight against COVID-19
• Reinventing Healthcare in Asia
• Accelerating Efforts to Combat the Global COVID-19 Pandemic
• Evaluation of Stem Cell Therapy as Potential Treatment for COVID-19 Pneumonia
• Factors that Influence Hybrid Cloud Adoption in Healthcare Organizations
• Commencing Sale of Nutraceuticals in China
• Research Collaboration to Develop Novel Therapies
• Easing Strain on Healthcare Systems with COVID-19 Patient Management Platform

For the month of July 2020, APBN will explore implications the COVID-19 outbreak will have on food security in the Asia Pacific Region. In the Columns dive into primary care in the healthcare system looking at the importance of networks and increasing productivity through digitization. Read more on the interview with Associate Professor Jeremy Lim, founder of Southeast Asia's first stool bank, AMILI as the team works towards generating an understanding of the Asian gut microbiome.